DESCRIPTION The aim of Core B is to provide innovative technologies to profile RAS mutation-specific effector signaling. RAS effector signaling is complex and involves RAS interaction with a multitude (>10) of functionally diverse downstream effectors. While our current understanding of RAS effector utilization is advanced, it is also far from complete. To date, four effector families have been implicated in driving RAS-dependent cancer initiation and growth. Each effector network includes protein kinases. There is also significant crosstalk between the effector networks. Furthermore, these networks are highly dynamic, with complex feed-forward and feedback mechanisms. Classically, RAS effector signaling is profiled by evaluation of the two canonical effector pathways, the RAF-MEK-ERK mitogen-activated protein kinase cascade and the PI3K-AKT-mTOR prosurvival signaling network, using the phosphorylated state of ERK and AKT as readouts. However, it is now clear that these analyses alone fail to provide an adequate determination of RAS effector signaling. Since a major goal of this Program Project is the determination of RAS mutant-specific effector signaling, unbiased kinome-wide analyses are needed to accomplish this goal. Core B provides two innovative proteomics-based experimental platforms to accomplish this. First, Multiplexed Inhibitor Beads (MIBs) and Mass Spectroscopy (MIB/MS) analyses provide kinome-wide profiling of dynamic changes in protein kinase activity. Our preliminary studies applying MIB/MS to characterize such changes upon KRAS suppression identified protein kinases not previously known as components of RAS effector signaling, demonstrating the potential for this platform to identify novel RAS effector signaling outputs. Second, Reverse Phase Protein Array (RPPA) analyses will profile RAS-dependent changes in protein phosphorylation and activation states in cancer cell signaling networks. Additionally, a recently developed innovative advance in RPPA enabling the profiling of the activation state of interacting proteins will also be applied. The types of data generated using each experimental platform are highly complementary. We expect that together they will define novel RAS mutation- specific effector signaling networks.